# ******************************************************************************
# Copyright 2017-2018 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ******************************************************************************
import nltk
from nltk.corpus import wordnet as wn
from nlp_architect.data.cdc_resources.data_types.wn.wordnet_page import WordnetPage
from nlp_architect.utils.string_utils import StringUtils
[docs]class WordnetOnline(object):
def __init__(self):
self.cache = dict()
nltk.download("wordnet")
[docs] def get_pages(self, mention):
if mention.tokens_str in self.cache:
return self.cache[mention.tokens_str]
head_synonyms, head_names_derivationally = self.extract_synonyms_and_derivation(
mention.mention_head
)
head_lemma_synonyms, head_lemma_derivationally = self.extract_synonyms_and_derivation(
mention.mention_head_lemma
)
clean_phrase = StringUtils.normalize_str(mention.tokens_str)
all_clean_words_synonyms = self.all_clean_words_synonyms(clean_phrase)
wordnet_page = WordnetPage(
mention.tokens_str,
clean_phrase,
mention.mention_head,
mention.mention_head_lemma,
head_synonyms,
head_lemma_synonyms,
head_names_derivationally,
head_lemma_derivationally,
all_clean_words_synonyms,
)
self.cache[mention.tokens_str] = wordnet_page
return wordnet_page
[docs] @staticmethod
def extract_synonyms_and_derivation(word):
lemma_names = set()
derivationally_related_forms = set()
for synset in wn.synsets(word):
for lemma in synset.lemmas():
lemma_name = lemma.name().replace("_", " ")
if not StringUtils.is_stop(lemma_name.lower()):
lemma_names.add(lemma_name)
derivationally_related_forms.update(
[
lem.name().replace("_", " ")
for lem in lemma.derivationally_related_forms()
if not StringUtils.is_stop(lem.name().lower())
]
)
return lemma_names, derivationally_related_forms
[docs] @staticmethod
def all_clean_words_synonyms(clean_phrase):
words = clean_phrase.split()
return [
set(
[
lemma.lower().replace("_", " ")
for synset in wn.synsets(w)
for lemma in synset.lemma_names()
if not StringUtils.is_stop(lemma.lower())
]
)
for w in words
]